This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Title: "TCF7L2 promoter variants in Oklahoma American Indians" Type 2 diabetes is a growing public health crisis in the United States. In Oklahoma, having American Indian ancestry triples the risk of being diagnosed with diabetes. Variants in the TCF7L2 and other genes are associated with increased diabetes risk in populations with European, Chinese, and Middle Eastern ancestry, but their role in diabetes risk in American Indians is not well understood. Our current understanding of diabetes risk in American Indians is based largely on information from American Indians in Arizona. Because of genetic, cultural, and environmental differences between American Indian populations, American Indians in Oklahoma may have risk factors not identified in Arizona populations. Conversely, factors identified in Oklahoma groups may contribute to diabetes risk in other American Indian populations, and in the US population as a whole. In addition, more than half of the Oklahoma Indians in the study sample have at least one ancestor of European origin, suggesting that TCF7L2 variants that are associated with diabetes risk in European populations may also contribute significantly to diabetes risk in American Indians in Oklahoma. In this one year pilot study, we are using gene sequencing to identify variants in the TCF7L2 promoter region that potentially contribute to type 2 diabetes risk in American Indians in Oklahoma. Our study uses DNA that was previously collected from 96 individuals (192 chromosomes) with type 2 diabetes. As a part of another study, we are evaluating polymorphisms in a different part of the TCF7L2 gene (the exonic and intron-exon boundary regions) of these same individuals. After sequencing, we will use statistical genetic methods to infer haplotypes and to evaluate linkage disequilibrium between the variants. Our goal is to identify variants that can be used as the basis for future gene-specific association analyses, and for investigations of the contribution of genotype-by-environment interaction effects to diabetes risk. The results of this investigation may help us to understand how diabetes develops, and over the long term are potentially useful for creating culturally-appropriate diabetes interventions and treatments.